

from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


a__												=						logging.get_logger(__name__)

a__												=						{
    '''YituTech/conv-bert-base''': '''https://huggingface.co/YituTech/conv-bert-base/resolve/main/config.json''',
    '''YituTech/conv-bert-medium-small''': (
        '''https://huggingface.co/YituTech/conv-bert-medium-small/resolve/main/config.json'''
    ),
    '''YituTech/conv-bert-small''': '''https://huggingface.co/YituTech/conv-bert-small/resolve/main/config.json''',
    # See all ConvBERT models at https://huggingface.co/models?filter=convbert
}

class 							UpperCAmelCase_						(     __lowercase  ):
							"""simple docstring"""







							UpperCAmelCase__			:		Any             =       "convbert"


							def __init__(     self			,				_a=3_0_5_2_2			,				_a=7_6_8			,				_a=1_2			,				_a=1_2			,				_a=3_0_7_2			,				_a="gelu"			,				_a=0.1			,				_a=0.1			,				_a=5_1_2			,				_a=2			,				_a=0.02			,				_a=1e-1_2			,				_a=1			,				_a=0			,				_a=2			,				_a=7_6_8			,				_a=2			,				_a=9			,				_a=1			,				_a=None			,				**_a			,				)       ->       Tuple:
												super().__init__(
												    pad_token_id=_a			,				bos_token_id=_a			,				eos_token_id=_a			,				**_a			,				)

												_a      :       Optional[int]   =						vocab_size
												_a      :       str   =						hidden_size
												_a      :       str   =						num_hidden_layers
												_a      :       List[Any]   =						num_attention_heads
												_a      :       int   =						intermediate_size
												_a      :       List[str]   =						hidden_act
												_a      :       Union[str, Any]   =						hidden_dropout_prob
												_a      :       Optional[Any]   =						attention_probs_dropout_prob
												_a      :       int   =						max_position_embeddings
												_a      :       str   =						type_vocab_size
												_a      :       Union[str, Any]   =						initializer_range
												_a      :       Union[str, Any]   =						layer_norm_eps
												_a      :       str   =						embedding_size
												_a      :       Tuple   =						head_ratio
												_a      :       Tuple   =						conv_kernel_size
												_a      :       Union[str, Any]   =						num_groups
												_a      :       Dict   =						classifier_dropout



class 							UpperCAmelCase_						(     __lowercase  ):
							"""simple docstring"""







							@property
							def 							__lowercase							(     self			)       ->       Mapping[str, Mapping[int, str]]:
												if self.task == "multiple-choice":
																	_a      :       Tuple   =						{0: '''batch''', 1: '''choice''', 2: '''sequence'''}
												else:
																	_a      :       Tuple   =						{0: '''batch''', 1: '''sequence'''}
												return OrderedDict(
												    [
												        ('''input_ids''', dynamic_axis),
												        ('''attention_mask''', dynamic_axis),
												        ('''token_type_ids''', dynamic_axis),
												    ]			)










'''simple docstring'''






from __future__ import annotations

lowerCAmelCase									=   []






def   __A    (				a_    :	list[list[int]]   ,a_    :	int   ,a_    :	int						):
		for i in range(len(a_						)						):
				if board[row][i] == 1:
						return False
		for i in range(len(a_						)						):
				if board[i][column] == 1:
						return False
		for i, j in zip(range(a_   ,-1   ,-1						)   ,range(a_   ,-1   ,-1						)						):
				if board[i][j] == 1:
						return False
		for i, j in zip(range(a_   ,-1   ,-1						)   ,range(a_   ,len(a_						)						)						):
				if board[i][j] == 1:
						return False
		return True






def   __A    (				a_    :	list[list[int]]   ,a_    :	int						):
		if row >= len(a_						):
				solution.append(a_						)
				printboard(a_						)
				print()
				return True
		for i in range(len(a_						)						):
				if is_safe(a_   ,a_   ,a_						):
						lowerCAmelCase  :     Dict       						= 1
						solve(a_   ,row + 1						)
						lowerCAmelCase  :     Optional[int]       						= 0
		return False






def   __A    (				a_    :	list[list[int]]						):
		for i in range(len(a_						)						):
				for j in range(len(a_						)						):
						if board[i][j] == 1:
								print("Q"   ,end=" "						)
						else:
								print("."   ,end=" "						)
				print()


# n=int(input("The no. of queens"))
lowerCAmelCase									=   8
lowerCAmelCase									=   [[0 for i in range(n)] for j in range(n)]
solve(board, 0)

print("""The total no. of solutions are :""", len(solution))



